Direct Neighborhood Discriminant Analysis for Face Recognition
Joint Authors
Cheng, Miao
Wen, Jing
Fang, Bin
Tang, Yuan Yan
Source
Mathematical Problems in Engineering
Issue
Vol. 2008, Issue 2008 (31 Dec. 2008), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2008-09-01
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
Face recognition is a challenging problem in computer vision and pattern recognition.
Recently, many local geometrical structure-based techiniques are presented to obtain the low-dimensional representation of face images with enhanced discriminatory power.
However, these methods suffer from the small simple size (SSS) problem or the high computation complexity of high-dimensional data.
To overcome these problems, we propose a novel local manifold structure learning method for face recognition, named direct neighborhood discriminant analysis (DNDA), which separates the nearby samples of interclass and preserves the local within-class geometry in two steps, respectively.
In addition, the PCA preprocessing to reduce dimension to a large extent is not needed in DNDA avoiding loss of discriminative information.
Experiments conducted on ORL, Yale, and UMIST face databases show the effectiveness of the proposed method.
American Psychological Association (APA)
Cheng, Miao& Fang, Bin& Tang, Yuan Yan& Wen, Jing. 2008. Direct Neighborhood Discriminant Analysis for Face Recognition. Mathematical Problems in Engineering،Vol. 2008, no. 2008, pp.1-15.
https://search.emarefa.net/detail/BIM-501126
Modern Language Association (MLA)
Cheng, Miao…[et al.]. Direct Neighborhood Discriminant Analysis for Face Recognition. Mathematical Problems in Engineering No. 2008 (2008), pp.1-15.
https://search.emarefa.net/detail/BIM-501126
American Medical Association (AMA)
Cheng, Miao& Fang, Bin& Tang, Yuan Yan& Wen, Jing. Direct Neighborhood Discriminant Analysis for Face Recognition. Mathematical Problems in Engineering. 2008. Vol. 2008, no. 2008, pp.1-15.
https://search.emarefa.net/detail/BIM-501126
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-501126